Dataset: Data and Code for: "Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System"

Description: This dataset contains source data and program code used for validation of the Adaptive Multi-Interval Scale (AMIS) method described in the associated article.

---

DATASET CONTENTS:

1. Student_Grades_Literature_Grade9_Raw_Data.xlsx
   - Excel file containing raw data (average class grades) for Literature, Grade 9
   - Used to demonstrate AMIS with fixed boundary values (range [2, 5])
   - Volume: 1,379 records

2. Student_Grades_History_Grade11_Raw_Data.xlsx
   - Excel file containing raw data (average class grades) for History, Grade 11
   - Used to demonstrate classical AMIS model with empirically determined boundaries
   - Volume: 879 records

3.  Student_Grades_Literature_Grade9_Raw_Data_all_values.xlsx
    - Excel file containing AMIS normalization results for Literature, Grade 9 data
    - Demonstrates AMIS with fixed boundary values (range [2, 5])
    - Contains original grades and converted values using 3, 5, 9, 17-point AMIS models

4.  Student_Grades_History_Grade11_Raw_Data_all_values.xlsx
    - Excel file containing AMIS normalization results for History, Grade 11 data
    - Demonstrates **classical AMIS** with empirically determined boundaries
    - Contains original grades and converted values using 3, 5, 9, 17-point AMIS models

5. World_Bank_Nominal_GDP_All_Countries_2024.xlsx
   - Excel file containing raw data on nominal GDP of countries worldwide for 2024
   - Source:World Bank Open Data, indicator "GDP (current US$)"
   - Source link: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD
   - Used to demonstrate AMIS effectiveness on data with extreme distribution asymmetry

6. AMIS_Normalization_Demonstration_GDP_2024_Countries.xlsx
   - Excel file containing results of GDP data normalization using AMIS method and statistical analysis
   - Includes comparison of various AMIS models (3, 5, 9, 17 points) with linear normalization

7. AMIS_Data_Normalization_Converter_EN.py
   - Open source Python 3 program code
   - Implements the Universal Adaptive Normalization Scale (AMIS) algorithm

---

SOFTWARE CODE DESCRIPTION:

AMIS_Data_Normalization_Converter.py

This code implements the Universal Adaptive Normalization Scale (AMIS). 
AMIS is designed for transforming and normalizing heterogeneous data based on adaptive partitioning of the measurement range into multiple intervals using statistical characteristics of the sample. This enables more accurate and flexible scaling compared to conventional linear normalization.

Key features:
- Hierarchical computation of control points (3, 5, 9, 17 points)
- Construction of piecewise linear transformation function
- Visualization of original and normalized data
- Graphical user interface for ease of use

System requirements:
- Python 3.x
- pandas
- numpy
- scipy
- matplotlib
- tkinter (typically included in standard Python distribution)

Installation of dependencies:
Before running the code, install required libraries using pip:
```bash
pip install pandas numpy scipy matplotlib
```

Running instructions:
Execute the script in a Python runtime environment. The program will open a graphical window with step-by-step instructions for selecting data files and normalization parameters.

---

CITATION:
When using this dataset or code in your work, please cite both the original article and this dataset using the provided DOI.

---

CONTACT INFORMATION:
Gennadiy Grigorievich Kravtsov
Director of Research Center "Applied Statistics"
E-mail: 62abc@mail.ru
ORCID: https://orcid.org/0009-0000-3405-1461


